Identification of Consistent and Inconsistent Schools

Data Visualization in R – Final project

Marwin Carmo

Background

  • The Spike-and-Slab Mixed-Effects Location Scale model (SS-MELSM) methodology identifies clustering units (students, classrooms, etc.) that exhibit unusual levels of residual variability—such as consistency or inconsistency—in academic achievement.

  • Higher (>.75) Posterior Inclusion Probability (PIP) of the scale random effect is evidence of unusual variability.

  • ivd is a package that facilitates the implementation of the SS-MELSM.

Research question

How can we visualize these clusters to clearly highlight what makes them unique compared to others?

  • The final goal is to enhance the visualizations currently provided by the ivd package.

Method

  • Standardized math scores from 11,386 11th and 12th-grade students across 160 schools.

  • I will work with posterior estimates from the scale model:

    • PIPs,
    • random effects standard deviations,
    • within-school residual variance, and
    • math scores.

Improvements to existing plots

PIP plot

Old version

New version

Improvements to existing plots

Funnel plot

Old version

New version

Improvements to existing plots

Outcome plot

Old version

New version

New plots

Sugarloaf plots